Group-Specific Score Normalization for Biometric Systems

The problem of biometric menagerie, first pointed out by Doddington et al. (1998), is one that plagues all biometric systems. They observe that only a handful of clients (enrolled users in the gallery) actually contribute disproportionately to recognition errors. While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies, in this paper, the authors explore a novel category of approaches: group-specific score normalization. While client-specific score normalization can be negatively impacted by the paucity of genuine score samples, group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated.